// docs/ML_SYSTEM_ARCHITECTURE.md

ML System Architecture

Last Updated: June 7, 2025

Overview

The Anya Core ML system provides comprehensive machine learning capabilities integrated with Bitcoin protocols, Web5 technologies, and enterprise-grade security. The system follows a modular agent-based architecture with real-time system mapping and federated learning capabilities.

Core Components

ML Pipeline

The ML pipeline implements a comprehensive data processing workflow:

┌─────────────┐    ┌──────────────┐    ┌─────────────┐    ┌─────────────┐
│             │    │              │    │             │    │             │
│ Data Source │───▶│ Data Pipeline│───▶│ ML Processing│───▶│ Data Sink   │
│             │    │              │    │             │    │             │
└─────────────┘    └──────────────┘    └─────────────┘    └─────────────┘
                                           │    ▲
                                           │    │
                                           ▼    │
                                      ┌────────────────┐
                                      │                │
                                      │  Model Store   │
                                      │                │
                                      └────────────────┘
  • Data Ingestion: Bitcoin blockchain data, Web5 protocol data, market data
  • Preprocessing: Data cleaning, normalization, feature extraction
  • Model Training: Supervised, unsupervised, and reinforcement learning
  • Validation: Cross-validation, backtesting, performance metrics
  • Inference: Real-time prediction and analysis
  • Metrics Collection: Performance monitoring and system health

Agent System

The agent system provides distributed AI coordination and management:

Core Agents

  • MLCoreAgent: Primary ML coordination and task distribution
  • DataPipelineAgent: Data ingestion, processing, and quality assurance
  • ValidationAgent: Model validation, testing, and performance monitoring
  • NetworkAgent: Distributed learning coordination and communication

Enterprise Agents

  • AnalyticsAgent: Advanced analytics and business intelligence
  • ComplianceAgent: Regulatory compliance and audit management
  • SecurityAgent: Security monitoring and threat detection

Integration Agents

  • BlockchainAgent: Bitcoin and Layer 2 protocol integration
  • Web5Agent: Web5 protocol handling and DID management
  • ResearchAgent: Research monitoring and innovation tracking
  • FederatedAgent: Federated learning coordination across nodes

Validation Framework

Multi-layered validation ensures system reliability and accuracy:

  • Data Validation: Input data quality, consistency, and completeness
  • Model Validation: Model accuracy, bias detection, performance metrics
  • System State Validation: Real-time system health and state consistency
  • Performance Validation: Latency, throughput, and resource utilization

Metrics System

Comprehensive metrics collection and monitoring:

  • Performance Metrics: Model accuracy, inference latency, throughput
  • System Health Metrics: CPU, memory, disk, network utilization
  • Business Metrics: ROI, cost per prediction, user engagement
  • Validation Metrics: Data quality scores, model drift detection

Integration Points

Blockchain Integration

Bitcoin Core

  • Transaction Analysis: Real-time transaction pattern analysis
  • Network Health Monitoring: Bitcoin network status and performance
  • Price Prediction: Advanced market analysis and forecasting
  • Security Analysis: Threat detection and vulnerability assessment

Lightning Network

  • Channel Analysis: Lightning channel state and performance monitoring
  • Payment Flow Analysis: Payment routing optimization
  • Liquidity Prediction: Channel liquidity forecasting
  • Fee Optimization: Dynamic fee calculation and optimization

Layer 2 Protocols

  • DLC Support: Discreet Log Contract analysis and oracle management
  • RGB Protocol: Asset tracking and validation
  • Stacks Integration: Smart contract analysis and optimization
  • RSK Integration: Sidechain monitoring and cross-chain analysis

Web5 Integration

DID Management

  • Identity Analysis: Decentralized identity pattern analysis
  • Credential Validation: Verifiable credential verification
  • Privacy Preservation: Zero-knowledge proof integration
  • Identity Fraud Detection: Advanced fraud detection algorithms

Data Storage

  • DWN Integration: Decentralized Web Node data management
  • Data Quality Assessment: Automated data quality scoring
  • Storage Optimization: Intelligent data placement and replication
  • Access Pattern Analysis: User behavior analysis and optimization

Protocol Handling

  • Protocol Compliance: Web5 protocol validation and monitoring
  • Performance Optimization: Protocol performance tuning
  • State Management: Distributed state consistency management
  • Event Processing: Real-time event stream processing

Research Integration

Literature Analysis

  • Paper Monitoring: Automated research paper analysis and categorization
  • Trend Detection: Emerging technology trend identification
  • Knowledge Graph: Dynamic knowledge graph construction and maintenance
  • Citation Analysis: Research impact assessment and ranking

Code Repository Monitoring

  • GitHub Integration: Repository monitoring and analysis
  • Code Quality Assessment: Automated code quality scoring
  • Security Vulnerability Detection: CVE monitoring and assessment
  • Innovation Tracking: New feature and capability detection

Protocol Updates

  • BIP Monitoring: Bitcoin Improvement Proposal tracking and analysis
  • Web5 Updates: Web5 protocol evolution tracking
  • Standard Compliance: Multi-protocol compliance monitoring
  • Impact Assessment: Change impact analysis and prediction

Innovation Tracking

  • Technology Radar: Emerging technology monitoring
  • Patent Analysis: Patent landscape analysis and IP monitoring
  • Competitive Intelligence: Market and competitor analysis
  • Investment Tracking: Venture capital and funding analysis

System Architecture

Agent Checker System (AIP-002) ✅

The Agent Checker System implements the "read first always" principle:

┌────────────────────┐    ┌─────────────────────┐    ┌────────────────────┐
│                    │    │                     │    │                    │
│   Input Sources    │───▶│   Agent Checker     │───▶│   System Actions   │
│                    │    │                     │    │                    │
└────────────────────┘    └─────────────────────┘    └────────────────────┘
                               │       ▲
                               │       │
                               ▼       │
                          ┌────────────────┐
                          │                │
                          │    In-Memory   │
                          │    State       │
                          │                │
                          └────────────────┘

System Mapping and Indexing

Real-time system mapping provides comprehensive system awareness:

  • Component Registration: Dynamic component discovery and registration
  • Dependency Tracking: Real-time dependency graph maintenance
  • Health Monitoring: Component health tracking and alerting
  • Performance Metrics: System-wide performance monitoring
  • Relationship Management: Agent relationship tracking and optimization

Federated Learning Architecture

Distributed learning across multiple nodes:

┌─────────────┐    ┌─────────────┐    ┌─────────────┐
│   Node A    │    │   Node B    │    │   Node C    │
│  Local ML   │◄──►│  Local ML   │◄──►│  Local ML   │
│   Model     │    │   Model     │    │   Model     │
└─────────────┘    └─────────────┘    └─────────────┘
        │                  │                  │
        │                  │                  │
        └──────────────────┼──────────────────┘
                           │
                    ┌──────▼──────┐
                    │             │
                    │ Federated   │
                    │ Coordinator │
                    │             │
                    └─────────────┘
  • Privacy-Preserving: Secure multi-party computation protocols
  • Model Aggregation: Advanced federated averaging algorithms
  • Byzantine Fault Tolerance: Robust consensus mechanisms
  • Differential Privacy: Privacy-preserving machine learning

Data Flow

Input Data Sources

  • Bitcoin Blockchain: Transaction data, block data, network statistics
  • Lightning Network: Channel data, payment data, routing information
  • Web5 Protocols: DID data, DWN data, credential data
  • Market Data: Price data, volume data, orderbook data
  • External APIs: Social media, news feeds, economic indicators

Processing Pipeline

  1. Data Ingestion: Multi-source data collection and normalization
  2. Quality Assessment: Data quality scoring and validation
  3. Feature Engineering: Advanced feature extraction and selection
  4. Model Training: Distributed model training and optimization
  5. Validation: Comprehensive model validation and testing
  6. Deployment: Model deployment and serving infrastructure
  7. Monitoring: Real-time performance monitoring and alerting

Output Destinations

  • Dashboard: Real-time analytics and visualization
  • API Endpoints: RESTful and GraphQL API access
  • Alerts: Real-time alerting and notification system
  • Reports: Automated report generation and distribution
  • Integration: Direct integration with other system components

Security and Compliance

Security Framework

  • Encryption: End-to-end encryption for all data transmission
  • Authentication: Multi-factor authentication and access control
  • Authorization: Role-based access control (RBAC)
  • Audit Logging: Comprehensive audit trail and compliance logging
  • Threat Detection: Real-time security threat detection and response

Compliance Standards

  • GDPR: European data protection regulation compliance
  • SOX: Sarbanes-Oxley compliance for financial data
  • HIPAA: Healthcare data protection compliance
  • ISO 27001: Information security management standards
  • Bitcoin Standards: BIP compliance and Bitcoin protocol adherence

Privacy Protection

  • Differential Privacy: Statistical privacy preservation
  • Homomorphic Encryption: Computation on encrypted data
  • Secure Multi-Party Computation: Collaborative computation without data sharing
  • Zero-Knowledge Proofs: Privacy-preserving verification protocols

Performance and Scalability

Performance Optimization

  • Model Optimization: Advanced model compression and optimization
  • Caching: Intelligent caching strategies for improved performance
  • Load Balancing: Dynamic load balancing across computing resources
  • Resource Management: Efficient resource allocation and utilization

Scalability Features

  • Horizontal Scaling: Auto-scaling across multiple nodes
  • Vertical Scaling: Dynamic resource scaling based on demand
  • Edge Computing: Edge deployment for reduced latency
  • Cloud Integration: Multi-cloud deployment and management

Monitoring and Observability

  • Real-time Metrics: Comprehensive real-time monitoring
  • Distributed Tracing: End-to-end request tracing
  • Log Aggregation: Centralized log collection and analysis
  • Alerting: Intelligent alerting and notification system

Future Roadmap

Short-term (Q2 2025)

  • Enhanced federated learning capabilities
  • Advanced privacy-preserving techniques
  • Improved real-time processing performance
  • Extended Web5 integration features

Medium-term (Q3-Q4 2025)

  • Quantum-resistant cryptography integration
  • Advanced AI/ML model capabilities
  • Cross-chain analytics expansion
  • Enhanced compliance automation

Long-term (2026+)

  • Autonomous system management
  • Advanced AGI integration
  • Global federated network expansion
  • Next-generation protocol support

This documentation follows the AI Labeling Standards based on official Bitcoin Improvement Proposals (BIPs).}